Nowadays, mobile apps have become an indispensable part of modern human life. In July 2014, there are about 1.3 million apps and 1.2 million apps in Google Play app store and Apple App Store, respectively. As the number of apps is huge, it is extremely hard for the users to find apps without recommendation functions. The term app or application is “a computer program designed for a specific task or use”. In other words, the app is defined by app functions that enable the users to perform specific tasks. The app function refers to a content page or functionality in a mobile app, such as “a restaurant's reviews” in Yelp, or “get directions” from one place to another in Google Map.
Recommendation systems play an important role in human life, greatly facilitating people's daily lives through providing information to the users. The recommendation systems suggest items based on user profile without asking for the user's needs. The recommendation systems are generally classified into two major systems: collaborative filtering systems and content-based recommendation systems. The collaborative filtering systems recommend items that other users with similar tastes preferred in the past while the content-based systems generally recommend items similar to those preferred by the users in the past. The recommendation systems may be more convenient for the user since the user does not need to input his or her needs.
However, considering an app usually consists of a lot of content pages or functionalities (defined as app functions), it is often tedious for the user to reach for a specific app function even if an app is open before the user clicks an app icon, it still needs to take a while for the user to reach for the needed content page or functionality inside the app. In current studies, few methods collect text data in the app functions as clues to make prediction.
The disclosed methods and systems are directed to solve one or more problems set forth above and other problems.